CCF: EAGER: Collaborative Research: Scalable Graph Mining and Clustering on Desktop Supercomputers

真实的世界数据，如万维网、社交网络、企业知识网络、生物网络、语义网络等，可以抽象成一个庞大而复杂的图，有数百万到数十亿的节点和边。随着这些数据的爆炸式增长，迫切需要数据挖掘、分析和查询工具来快速理解和提取知识。然而，有效地利用现代架构的资源并挖掘如此大的图形以获得有趣的模式仍然具有挑战性。同时，具有多核处理器和图形处理器的商用台式机架构（拥有数百个流核心）正在为桌面上的并行图分析和管理开辟重要的机会。这项探索性研究旨在提升现代桌面超级计算机上的图挖掘和聚类算法的性能，以利用配备图形处理器的多核系统的强大功能，以及探索和开发用于减少搜索空间和所处理的数据量的新算法。

Real world data, such as World Wide Web, social networks, corporate knowledge networks, biological networks, semantic networks, etc., can be abstracted in the form of a massive and complex graph, with millions to billions of nodes and edges. With the explosion of such data, there is a pressing need for data mining, analysis, and querying tools to rapidly make sense of and extract knowledge. However, effectively leveraging the resources of modern architectures and mining such large graphs for interesting patterns remains challenging. At the same time commodity desktop architectures that have processors with multiple cores and graphics processors (with hundreds of stream cores) are opening up significant opportunities for parallel graph analytics and management on the desktop.This exploratory research seeks to scale up the performance of graph mining and clustering algorithms on modern desktop supercomputers to leverage the power of multi-core systems equipped with graphics processors, and to explore and develop new algorithms for reducing the search space and and the amount of data processed.